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1. 2015 IBM Presentation to University of California Irvine Dr. Arvind Sathi February 25, 2015 2. 2015 IBM2 The Dance Vacation Product Idea A vacation for dance enthusiasts Using the DWTS format Complete with Disney costumes On Disney Cruise Line Concept 3. 2015 IBM3 What this scenario demonstrates A high value, high margin business opportunity A micro-segment of customers which can not be reached via broad marketing campaigns A combination of Disney and external data, correlated to formulate the product, and the campaign A custom defined ecosystem which gets access to this product and related campaigns A set of interactions geared towards specific micro-segments. 4. 2015 IBM4 Overview Changing Winds Proposition 1: From Sample recalls to Observing the Population Proposition 2: Marketing through Collaborative Influence Proposition 3: From siloed to Orchestrated Marketing Technological Enablers Changes to Marketing Ecosystem and Organization 5. 2015 IBM5 Changing Winds Rise of Digital Society Ubiquitous use of Mobile Platform Savvy customers discover Social Computing Crowd-sourced analytics tools Monetization Private and public clouds Customer preferences and privacy concerns 6. 2015 IBM6 How was your first marketing exposure to the Social Media? 7. 2015 IBM7 Internet of Things Ecosystem Map from Beecham Research Source: M2M/IoT Sector Map by Beecham Research 8. 2015 IBM8 Monetization of data emergence of a market place, reprinted with permission 9. 2015 IBM9 Proposition 1: From Sample recalls to Observing the Population Census data Social media data Location data Product usage data Shopping data Conversation data Purchase data 10. 2015 IBM10 Data Cell tower locations Wi-fi locations Device locations Device usage data apps, web sites Customer data demographics Refined locations Mobility Patterns Hang outs Hang outs correlated with business locations Mode of transportation Traveling buddies Analytics Location Data 11. 2015 IBM11 Discovery from location data A typical discovery uses statistical tools to identify pattern in data. Discovery may contribute new derived attributes for further analysis or reporting. Night Owls at Night Delivery People During the Day Quiet Weekday people go for dinner on weekends Almost no Homebodies any time 12. 2015 IBM12 Buddies, Hangouts, Sofa Surfers Three areas of analysis: n Subscriber level Lifestyle and Mobility profiles n Popular Locations with specific profiles n Subscriber Pairings or Buddies Who Are You? Homebody Daily Grinder Delivering the Goods Globetrotter Sofa Surfer 10 Top Hangouts Best BuddiesID Rank Night Morning Lunch Dinner Breakfast Afternoon Total Result 54796109xxx 1 34 7 11 15 9 12 88 54809186xxx 2 33 7 11 15 9 12 87 30931430xxx 3 32 7 11 15 9 12 86 54802704xxx 4 31 7 11 15 9 12 85 54796392xxx 5 29 5 11 15 6 11 77 13. 2015 IBM13 Competitive Locations Have Different Profiles of Traffic Throughout the Day Location of Latte Land is very close to Starbucks, but has more evening traffic Time of Day Store Visits per interval 14. 2015 IBM14 Subscriber URL Activity Mined to Create Interest Profile - Use Social Media (Twitter) data to create profiles Soccer: User interest in soccer, favorite teams Telco: Services provided by Telco Others: Users viewing experience, Users comments on Apps including what they like/dislike - Research URL Analytics asset and Tag Cloud asset Identify categories user will be interested in based on URL analytics Identify word clouds based on pages associated with category Interest Profile 15. 2015 IBM15 System U / Deriving Personality Profile Psycho-linguistic Profile 16. 2015 IBM16 Group with no leader Social Network using Voice Call Data 17. 2015 IBM17 Slice and Dice of my purchase data, reprinted with permission 18. 2015 IBM18 How can this be utilized by Marketers Amazon Apple iTunes PayPal eBay Target Groupon Living Social Netix Google Play Best Buy Newegg Walmart Zappos Woot, reprinted with permission 19. 2015 IBM19 Building Context and Intent from Location data Deriving location: location information may be derived using multi- modal information CDR data, tower data, device data, Wi-fi etc. Accuracy of location information depends on data fidelity etc. Building context: making sense of the location information Correlate location information with business data Various other correlation rules may be used to build a rich context Inferring intent: infer consumer level intents by leveraging location and mobility patterns Deriving Location Inferring IntentBuilding Context 20. 2015 IBM20 Proposition 2: Marketing through Collaborative Influence Personalized customer / product research Online advertising Multi-channel shopping Intelligent campaigns Big ticket items and auction / negotiation markets Games, videos, smart phones and tablets Influence through crowd-sourced reviews Endorsements and viral buzz 21. 2015 IBM21 Customer Needs and Usage Mapped to Products Customers Needs Usage Offerings Components Micro Segment 22. 2015 IBM22 Customer Needs and Usage Mapped to Products Customers Needs Usage Offerings Components Day time Work at Home Work day High Usage Off time Low Usage Home Office Bandwidth Network Policy 23. 2015 IBM23 A not so intelligent campaign 24. 2015 IBM24 Drive Interactwiththe customertoseek permissiontouseloca3on informa3onandsend campaign,record interac3onandresults. Discover Collecthistorical behavioraldata,past acts,andsuccessrates. Analyzehistoricaldata toformulatepa?erns andchangesrequired todetect,and inves3gatesteps Decide Usebackground informa3on,past campaigns,privacy preferences,customer reac3ontopast campaigns,purchase intent,preferences expressedinsocial mediatodesign campaign. Detect Detectinreal3meifa transac3onrelatesto targetedsubscribers. Iden3fy,align,score, andsendforfurther processing(e.g.,a targetedcustomer drivingtowardsmall) Smarter Campaigns using D4 Detectobserva,ons aboutatarget Takeac,oninreal ,mewhenit ma8ers Findnewtargetsby analyzinghistorical data Iden,fypa8erns over,meand ac,onsrequired Drive Detect Discover Decide Target Subscriber 24 25. 2015 IBM25 Digital Advertising Marketplace Publisher Advertisers Supply Side Platform (SSP) Demand Side Platform (DSP) Data Management Platform (DMP) Represents publishers, and runs auctions for inventory in real-time, finding the highest bidder Represents brands, and bids on auctions for inventory in real-time, finding the best price / consumer propensity match Sources data wherever it can to help DSPs in particular to make better predictions about inventory so that they can be more certain about the likely customer intent, and therefore bid higher and secure more conversions. 26. 2015 IBM26 Google India advertisement goes viral Published on Nov 13, 2013 Partitions divide countries, friendships find a way (Use captions to translate the film in 9 languages including French, Malayalam and Urdu) The India-Pakistan partition in 1947 separated many friends and families overnight. A granddaughter in India decides to surprise her grandfather on his birthday by reuniting him with his childhood friend (who is now in Pakistan) after over 6 decades of separation, with a little help from Google Search. Views It is a 3 minute, 32 second advertisement that would be considered too long for a conventional advertisement. It shows the Google products being used in a use case, and it attracted more than 3 million viewers in the first three days it was posted. 27. 2015 IBM27 Proposition 3: From siloed to Orchestrated Marketing Customer profile Entity resolution Personal privacy preference management Dynamic pricing Orchestration for context-based advertising and promotion Cross-channel co-ordination Market tests 28. 2015 IBM28 Dance Vacation product requires a single customer profile connecting diverse interests. 28 A vacation for dance enthusiasts Using the DWTS format Complete with Disney costumes On Disney Cruise Line ConceptDataServices Facebook posts Mobility patterns Hang outs Social circles Linear views Non-linear views Likes Past responses Past purchases Likes Shares Past purchases Interests Fan advocacy Dance Studio partnership Ads via non- linear Campaigns across touch points Campaigns across touch points Customer Profile 29. 2015 IBM29 A Multi-dimensional Customer Profile A comprehensive data model should capture a wide range of multi- dimensional and comprehensive information, adequate to reflect the customers complete digital profile Descriptive data Interaction data Real Time Alerts and NBA Privacy and Contact Preferences Contextual Multichannel Profile Partner Sectors 3rd Party Data Attitudinal data SentimentsCustomer Experience Profile Permissions & Data Privacy QoS Scores Behavioral Data OTT FavoritesMobility ProfilesUsage and ARPU Profile Mobile Payments Digital Account Portrait Digital Signatures Onboarding and Retention Personalizations SmartHome Subscriptions Red Flags Financial & Billing Profile Customer Lifetime Value Top Up Wallet 30. 2015 IBM30 Step 1: Identifying a high value target progressively Annon.ID ProleInforma;on Source AB1234 None AnnonID ProleInforma;on Source AB1234 Interestedincertaintypes ofphones Website Phonepage AB1234 Interestinapar,cular phone Website Search Interestedin4GPhone Website Use of Customer Profile in Digital Advertising 31. 2015 IBM31 Step 2: User visits their favorite News site (Increase Brand Experience) Offer 1 Offer 2 Offer 3 SmartPhone advertisement w/ Fashion callout 4G benefits advertisement Generic Offertel advertisement $1.50 $2.50 $12.00 Profile Information Source Offertel homepage view Website SmartPhone product page Website 4G coverage eligible Website 4G Ad Creative Impression Turn 4G Creative Ad Click Turn Offertel landing pag